A stochastic neighborhood conditional autoregressive model for spatial data.
A stochastic neighborhood conditional autoregressive model for spatial data.
复制标题
空间数据的随机邻域条件自回归模型。
DOI：
10.1016/j.csda.2008.08.010
复制
发表时间：
2009
影响因子：
1.8
通讯作者：
Ghosh,SujitK
中科院分区：
文献类型：
--
作者：
White,Gentry;Ghosh,SujitK
在一个格子或一组不规则区域上观察到的空间过程通常使用条件自回归（CAR）模型来建模。CAR模型内的邻域通常使用区域之间的间距或边界确定性地形成。本文提出了一种扩展的CAR模型，其中邻域的选择依赖于未知参数。这种扩展被称为随机邻域CAR（SNCAR）模型。由此产生的模型在准确估计从各种空间协方差模型生成的数据的协方差结构方面显示出灵活性。具体的例子说明使用从一些常见的空间协方差函数以及真实的数据在瑞士的土壤放射性污染后，切尔诺贝利事故产生的数据。
A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.